Basic Plots
Time Series
matrixTimeSeries = cbind(unlist(sapply(1:100, function (x) {randomTimeSeries(200,velocity=1,noise=0)})))
rplot::r.plot(matrixTimeSeries)
rplot::r.plot(x=seq(0,1,1/199), y=matrixTimeSeries)
matrixTimeSeries = cbind(unlist(sapply(1:5, function (x) {randomTimeSeries(50,velocity=1,noise=0)})))
r.plot(matrixTimeSeries)
r.plot.add(matrixTimeSeries, type='p')
Lines I
x = seq(0,10,0.1)
y = sin(seq(0,10,0.1))
z = cos(seq(0,10,0.1))
r.plot(x, y, type='l')
Lines II
r.plot.new(xlim=c(0,10), ylim=c(-1,1),xlab="x",ylab="y")
r.plot.add(x, y, type='l', icol=1)
r.plot.add(x, z, type='l', icol=2)
r.plot.new(xlim=c(0,10), ylim=c(-1,1))
r.plot.add(x, y, type='l', col=1)
r.plot.add(x, z, type='l', col=2)
r.plot.new(xlim=c(0,10), ylim=c(-1,1))
r.plot.add(x, y, type='l', col=rgb(0.8,0.8,0.0))
r.plot.add(x, z, type='l', col=rgb(0.0,0.8,0.8))
Points I
x = runif(500)-0.5
y = rnorm(500)-0.5
z = runif(500)-0.5
r.plot(y[1:100], type='p')
r.plot.add(y[1:100],type='l', col=rgb(0,0,0,0.1))
r.plot(x[1:10], y[1:10])
r.plot.add(x[1:10], y[1:10],type='l', col=rgb(0,0,0,0.1))
r.plot(x, y)
r.plot.new(x,y)
r.plot.add(x,y)
r.plot.new(c(x,x), c(x,y))
r.plot.add(x,y)
r.plot.add(x, z, icol=2)
Points II
r.plot.new(c(x,x), c(y,z))
r.plot.add(x, y, icol=1, alpha=0.3)
r.plot.add(x, z, icol=2, alpha=0.3)
r.plot.new(c(x,x), c(y,z))
r.plot.add(x, y, col=1, alpha=0.3)
r.plot.add(x, z, col=2, alpha=0.3)
r.plot.new(c(x,x), c(y,z))
r.plot.add(x, y, col=rgb(1,1,0), alpha=0.3)
r.plot.add(x, z, col=rgb(1,0,1), alpha=0.3)
r.plot(x, y, col=heat.colors(5))
r.plot(1:4, 1:4, col=terrain.colors(5), cex=20, alpha=0.6)
Other Plots
Third Axis
x = seq(0,10,0.5)
y = sin(x)
z = exp(x)
r.plot.new(x,y,thirdAxis=TRUE, main="Plot with 3rd Axis using new")
r.plot.add(x,y,type='l')
r.plot.coord(x, z)
r.plot.coord.axis(z)
r.plot.add(x,z,col=2,type='l')
r.plot(x,y,type='l',thirdAxis=TRUE, main="Plot with 3rd Axis")
r.plot.add(x,y,type='p')
r.plot.coord(x, z)
r.plot.coord.axis(z)
r.plot.add(x,z,col=2,type='l')
r.plot.add(x,z,col=2,type='p')
Bar plot
var = round(exp(runif(100)))
t = table(round(runif(100)),round(runif(100)))
rownames(t) = c("Tipo A", "Tipo B")
colnames(t) = c("Tipo A", "Tipo B")
r.plot.bar(var)
r.plot.bar(var, horizontal=TRUE)
r.plot.bar(table=t)
r.plot.bar(table=t, beside=TRUE)
r.plot.bar(table=t, horizontal=TRUE)
r.plot.bar(table=t, beside=TRUE, horizontal=TRUE)
r.plot.bar(table=t, beside=TRUE, horizontal=TRUE, background=FALSE, box=FALSE)
r.plot.bar(table=t, beside=TRUE, horizontal=TRUE, background=TRUE, box=FALSE)
Histogram plot
x = rnorm(2000)
r.plot.histogram(x)
r.plot.histogram(x, main="Density", freq=FALSE)
r.plot.histogram(x, breaks = 20)
Distribution plot
r.plot.distribution(x)
Heatmap
x = runif(5000)
y = runif(5000)
z = (x-0.7)^2+(y-0.6)^2
r.plot.heatmap(x,y,z)
r.plot.heatmap(x,y,z,mean)
data(volcano)
r.plot.heatmap(matrixData=volcano)
r.plot.heatmap(matrixData=volcano, palette=terrain.colors(12))
r.plot.heatmap(matrixData=volcano, contour=FALSE, palette=terrain.colors(12))
r.plot.heatmap(rnorm(10000), rnorm(10000),
xbreaks=30, ybreaks=30,
contour=FALSE,
palette=r.color.gradient.palette(c("white", r.color(1))))
r.plot.heatmap(rnorm(10000), rnorm(10000),
xbreaks=30, ybreaks=30,
contour=FALSE,
smooth=0.8,
palette=r.color.gradient.palette(c("white", r.color(1))))
Treemap
x = runif(1000)
f = cut(x, breaks = 10)
t = table(f)
segmentosNames <- names(t)
segmentosGroup <- names(t)
segmentosArea <- as.numeric(t)
segmentosColor <- runif(length(segmentosNames))
r.plot.treemap(
segment=segmentosNames,
segmentgroup=segmentosGroup,
area=segmentosArea,
color=segmentosColor,
colorScaleLeft=rgb(0.8,0.2,0.8), colorScaleCenter="White", colorScaleRight=rgb(0.2,0.2,0.8),
main="Tree Map")
Radial plot
x1 <- runif(5)
x2 <- 0.2+0.6*x1+0.2*runif(5)
df <- data.frame(groupASD = c("Blue Collar Communities", "Prospering Suburbs"), matrix(c(x1,x2), nrow = 2, byrow = TRUE))
colnames(df)[2:ncol(df)] <- c("A", "B", "C", "D", "E")
r.plot.radial(df, legend=FALSE)
r.plot.radial(df)
Graph plot
data(UKfaculty, package = "igraphdata")
x = as.matrix(igraph::get.adjacency(UKfaculty))
mat = matrix(runif(400), 20)
r.plot.heatmap(matrixData=mat, contour=FALSE)
r.plot.matrix(mat)
r.plot.matrix(-mat)
r.plot.matrix(mat-0.5)
r.plot.matrix(x)
r.plot.matrix.communities(x)
r.plot.matrix(r.plot.matrix.communities(x))
r.plot.heatmap(matrixData=r.plot.matrix.communities(x), contour=FALSE)
r.plot.graph.text(x, vertexLabelCex=0.5, edgeWidthMax=1.5)
Data Analysis
Data exploration
df <- airquality
df$Month = factor(df$Month)
df$Day = factor(sample(1:28, nrow(df), replace=TRUE))
r.plot.data(df)
r.export.dataoverview
## function (dades, col = NULL, horizontal = FALSE, label.cex = 0.7,
## label.rotation = 45, legend = FALSE, legend.pos = "topright",
## legend.cex = 0.6, folder = "output/", filePrefix = "img_",
## filePrefixCont = "cont_", filePrefixCat = "cat_", fileSufix = "",
## width = 480, height = 480, ...)
## {
## if (missing(col))
## col = r.palette.get()
## for (icol in 1:ncol(dades)) {
## strCol = names(dades)[icol]
## values = dades[, icol]
## if (is.numeric(values)) {
## png(paste0(folder, filePrefix, filePrefixCont, strCol,
## fileSufix, ".png"), width = width, height = height)
## r.plot.histogram(values = values, col = col[1], main = strCol,
## ...)
## dev.off()
## }
## else {
## png(paste0(folder, filePrefix, filePrefixCat, strCol,
## fileSufix, ".png"), width = width, height = height)
## r.plot.bar(values = values, col = col, horizontal = horizontal,
## label.cex = label.cex, label.rotation = label.rotation,
## legend = legend, legend.pos = legend.pos, legend.cex = legend.cex,
## main = strCol, ...)
## dev.off()
## }
## }
## }
## <environment: namespace:rplot>
Dimensionality Redution
r.plot2D.data(iris[,-5])
r.plot2D.pca(iris[,-5])
r.plot2D.nn(iris[1:50,-5])
km = kmeans(iris[,-5],3)
r.plot2D.data(iris[,-5], clustModel=km$cluster)
r.plot2D.pca(iris[,-5], clustModel=km$cluster)
# r.plot2D.nn(iris[1:50,-5], clustModel=km$cluster)
r.plot2D.data(iris[,-5], clustModel=km$cluster, clustReal=iris[,5])
r.plot2D.pca(iris[,-5], clustModel=km$cluster, clustReal=iris[,5])
# r.plot2D.nn(iris[,-5], clustModel=km$cluster, clustReal=iris[,5])
Binning
r.plot(iris$Petal.Width, iris$Petal.Length)
r.plot.burbujas
## function (datos, segmentacion, target, relativeToMean = FALSE,
## relativeToMeanX = FALSE, relativeToMeanY = FALSE, logScale = FALSE,
## logScaleX = FALSE, logScaleY = FALSE, laplaceSmooth = FALSE,
## showMean = FALSE, showMeanX = FALSE, showMeanY = FALSE, showTargetAbsMean = FALSE,
## sizeMin = 1, sizeMax = 3, xlab = "Vol.", ylab = "Freq.Rel.Target",
## ...)
## {
## require(rmodel)
## segmentacionValidated = NaN
## nelements = NaN
## listValues = list()
## for (ipos in 1:length(segmentacion)) {
## segment = segmentacion[ipos]
## var = datos[, segment]
## values = unique(var)
## listValues[[segment]] = values
## nelements[ipos] = length(values)
## }
## pos = 1
## elementsS = NaN
## elementsV = NaN
## finished = FALSE
## index = rep(1, length(segmentacion))
## while (!finished) {
## ind = 1:nrow(datos)
## for (ipos in 1:length(segmentacion)) {
## segment = segmentacion[ipos]
## ind = intersect(ind, which(datos[, segment] == listValues[[segment]][index[ipos]]))
## }
## if (length(ind) > 0) {
## if (laplaceSmooth) {
## elementsS[pos] = (1 + length(which(datos[ind,
## target] == 1)))/(1 + length(ind))
## elementsV[pos] = 1 + length(ind)
## pos = pos + 1
## }
## else {
## elementsS[pos] = length(which(datos[ind, target] ==
## 1))/length(ind)
## elementsV[pos] = length(ind)
## pos = pos + 1
## }
## }
## else if (laplaceSmooth) {
## elementsS[pos] = 1
## elementsV[pos] = 1
## pos = pos + 1
## }
## ipos = 1
## incrementFinished = FALSE
## while (ipos <= length(segmentacion) && !incrementFinished) {
## index[ipos] = index[ipos] + 1
## if (index[ipos] > nelements[ipos]) {
## index[ipos] = 1
## ipos = ipos + 1
## }
## else {
## incrementFinished = TRUE
## }
## }
## if (!incrementFinished)
## finished = TRUE
## }
## ind = 1:length(elementsS)
## ind = intersect(ind, which(!is.na(elementsS)))
## ind = intersect(ind, which(!is.infinite(elementsS)))
## if (logScale)
## ind = intersect(ind, which(elementsS != 0))
## burbujaS = elementsS[ind]
## burbujaV = elementsV[ind]
## meanS = mean(elementsS[ind])
## meanV = mean(elementsV[ind])
## if (relativeToMean) {
## burbujaS = burbujaS/meanS
## burbujaV = burbujaV/meanV
## }
## else {
## if (relativeToMeanY)
## burbujaS = burbujaS/meanS
## if (relativeToMeanX)
## burbujaV = burbujaV/meanV
## }
## if (logScale) {
## burbujaS = log10(burbujaS)
## burbujaV = log10(burbujaV)
## }
## else {
## if (logScaleY)
## burbujaS = log10(burbujaS)
## if (logScaleX)
## burbujaV = log10(burbujaV)
## }
## r.plot(x = burbujaV, y = burbujaS, cex = rmodel::r.normalize(x = elementsV[ind],
## imin = sizeMin, imax = sizeMax), type = "p", xlab = xlab,
## ylab = ylab, ...)
## if (showMean) {
## r.plot.add(x = c(meanV, meanV), y = c(-2 * (100 + max(abs(burbujaS))),
## 2 * (100 + max(abs(burbujaS)))), type = "l", col = rgb(0,
## 0, 0, 0.4))
## r.plot.add(y = c(meanS, meanS), x = c(-2 * (100 + max(abs(burbujaV))),
## 2 * (100 + max(abs(burbujaV)))), type = "l", col = rgb(0,
## 0, 0, 0.4))
## }
## else {
## if (showMeanX)
## r.plot.add(x = c(meanV, meanV), y = c(-2 * (100 +
## max(abs(burbujaS))), 2 * (100 + max(abs(burbujaS)))),
## type = "l", col = rgb(0, 0, 0, 0.4))
## if (showMeanY)
## r.plot.add(y = c(meanS, meanS), x = c(-2 * (100 +
## max(abs(burbujaV))), 2 * (100 + max(abs(burbujaV)))),
## type = "l", col = rgb(0, 0, 0, 0.4))
## }
## if (showTargetAbsMean) {
## meanTarget = rmodel::r.mean(datos[, target])
## r.plot.add(y = c(meanTarget, meanTarget), x = c(-2 *
## (100 + max(abs(burbujaV))), 2 * (100 + max(abs(burbujaV)))),
## type = "l", col = rgb(1, 0, 0, 0.3))
## }
## }
## <environment: namespace:rplot>
rmodel::r.segment(round(iris[,-5]), colnames(iris)[-5])
## [1] 1 6 6 21 1 16 6 21 7 21 16 21 6 7 3 18 1
## [18] 1 18 16 21 16 1 21 21 21 21 16 6 21 21 21 16 3
## [35] 21 6 3 1 7 21 1 12 7 121 16 6 16 6 16 6 144
## [52] 263 249 163 248 158 248 176 144 156 161 263 163 143 158 159 263 158
## [69] 268 163 248 158 253 143 158 159 144 249 263 158 163 163 158 248 261
## [86] 263 249 163 158 163 158 143 158 176 158 158 158 158 176 158 293 248
## [103] 294 293 293 310 266 294 299 289 248 248 294 253 248 248 293 305 310
## [120] 253 294 248 310 248 294 294 248 248 293 294 294 290 293 248 188 295
## [137] 293 293 248 249 294 249 248 294 294 249 253 248 248 248
K-means
km = kmeans(iris[,-5],3)
r.plot.kmeans.shapes(iris[,-5], km)
## K-means clustering with 3 clusters of sizes 38, 62, 50
##
## Cluster means:
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 6.850000000 3.073684211 5.742105263 2.071052632
## 2 5.901612903 2.748387097 4.393548387 1.433870968
## 3 5.006000000 3.428000000 1.462000000 0.246000000
##
## Clustering vector:
## [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [36] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [71] 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 1
## [106] 1 2 1 1 1 1 1 1 2 2 1 1 1 1 2 1 2 1 2 1 1 2 2 1 1 1 1 1 2 1 1 1 1 2 1
## [141] 1 1 2 1 1 1 2 1 1 2
##
## Within cluster sum of squares by cluster:
## [1] 23.87947368 39.82096774 15.15100000
## (between_SS / total_SS = 88.4 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
r.plot.kmeans.shapes(iris[,-5], km, paintCentroids=TRUE)
## K-means clustering with 3 clusters of sizes 38, 62, 50
##
## Cluster means:
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 6.850000000 3.073684211 5.742105263 2.071052632
## 2 5.901612903 2.748387097 4.393548387 1.433870968
## 3 5.006000000 3.428000000 1.462000000 0.246000000
##
## Clustering vector:
## [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [36] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [71] 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 1
## [106] 1 2 1 1 1 1 1 1 2 2 1 1 1 1 2 1 2 1 2 1 1 2 2 1 1 1 1 1 2 1 1 1 1 2 1
## [141] 1 1 2 1 1 1 2 1 1 2
##
## Within cluster sum of squares by cluster:
## [1] 23.87947368 39.82096774 15.15100000
## (between_SS / total_SS = 88.4 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"
r.plot.kmeans.smoothshapes(iris[,-5], km)
Model performance
x=runif(1000)
y=c(round(0.8*x[1:200]+0.2*runif(200)),round(0.6*x[201:700]+0.4*runif(500)),round(runif(300)))
rmodel::r.performance.metrics(x, y)
## ACTUAL_0 ACTUAL_1 PREDICTED PRECISION
## PREDICTED_0 398 107 505 0,78812
## PREDICTED_1 133 362 495 0,73131
## ACTUAL 531 469 1.000 0,75972
## RECALL 0,74953 0,77186 0,76069 0,76000
##
## F1 phi
## 1 0,76834 0,52041
## 2 0,75104 0,52041
## 3 0,75969 0,52041
##
## score
## Accuracy 0,76
## Precision 0,73131
## Recall (sensitivity) 0,77186
## specificity 0,74953
## Score F1 0,75104
## Score G 0,75131
## Score Beta 0,75104
## Score Phi (MCC) 0,52041
r.plot.confusionmatrix(x, y)
## ACTUAL_0 ACTUAL_1 PREDICTED PRECISION
## PREDICTED_0 398 107 505 0,78812
## PREDICTED_1 133 362 495 0,73131
## ACTUAL 531 469 1.000 0,75972
## RECALL 0,74953 0,77186 0,76069 0,76000
r.plot.F1(x, y)
## F1 phi
## 1 0,76834 0,52041
## 2 0,75104 0,52041
## 3 0,75969 0,52041
r.plot.roc(x,y)
r.plot.gain(x,y)
r.plot.lift(x,y)
